As we gradually return to the post-Covenant world, IT leaders need to develop their definition and evaluate industry trends. While many companies are transitioning to hybrids, updating business processes and adapting to customer needs, their creative skills, customer experience, launch of digital-enabled products, and mature machine learning may be backwards.

Digital transformation is not just about taking existing business processes and making them more efficient. New technology capabilities and growing customer opportunities require business leaders to rethink business models, products, targeted markets, and customer experiences. Your digital transformation may slip and require you to line up if you do not focus on growth and customer opportunities.

Digital Transformation Trends to Consider Now

Even healthy transformation programs and leaders need to understand what has changed and what is happening. Where do competitors perform smarter and faster than your organization? Which technological skills are becoming more important and are worth researching or conducting research-evidence? What process changes should your organization accelerate to prepare for new and changing opportunities?

Here are five trends that can drive you to adjust your digital transformation strategies.

1. Develop and grow customer information platforms

For organizations that are thinking of changing customer needs and driving more information, CDPs can be a strategic investment to improve organizational front-office experience. Organizations implementing these platforms aim to improve customer service, update product development strategies, improve sales priorities, and streamline marketing campaigns.

CDPs are primarily data storage and analytics tools that integrate customer information with CRM, ERPs, market automation tools, and performance systems. They provide a central view of the client’s profile, interactions, and events and serve as a two-way data storage repository with other client interaction systems. There are industry-based CDP solutions for retail and other B2C industries. Another option is to implement CDP in the main data management platform.

Implementing CDPs is complex due to the large number of stakeholders, business processes and systems that handle and process customer information. But a recent report from the CDP Institute shows that these programs are on the rise, with 52 percent of respondents feeding customer information to the central system, up from 37 in 2017. Make it possible for more companies.

[ Get exercises and approaches that make disparate teams stronger. Read the digital transformation ebook: Transformation Takes Practice. ]

2. Demonstrate business impact on machine learning investments

There are many reasons why organizations struggle to launch AI and machine learning programs or succeed in converting verification concepts. But according to a recent Kapmeni report, 53 percent of respondents will move beyond machine learning pilots and positions, up from 36 percent in 2017. Growth is not limited to information centers such as technology and financial services. ; Respondents from more than a dozen industries, often telecoms, utilities, and energy, have often deployed at least some limited AI.

Respondents in this report also report on business impacts on their AI investments. Of the respondents, 97 percent of those identified as IP leaders reported numerical benefits, while 39 percent of government benefits exceeded expectations.

While many organizations report skills gaps in machine learning, MLOps and ModelOps platforms help many organizations measure their machine learning processes, infrastructure, and practices. These platforms provide DevOps and SDLC capabilities for machine learning life cycles and are designed to help many organizations build, test, deploy, monitor and understand business value from machine learning investments.

With the growing number of machine learning life cycle platforms on the market, I expect it to be accessible to those who are slow to invest due to the required data and technology skills.

[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]

3. Update applications with hypothermia, low code and efficient strategies

Application development, modernization, and integration are central practices in digital transformation that help enterprises launch new business skills, improve customer experiences, and streamline business processes. Until recently, CIOs and IT leaders considered applications as a build-up decision or used the RPA platform to automate workflows. While building applications, many have made cloud-based micro-services and applications seamless and ripe. Covi then hit, and many IT leaders followed low-code and no-code platforms to accelerate app development.

It is important that there are many approaches to developing and supporting implementation development and integration, but today an increasing number of options provide a complete hypothesis platform. Hyperautomation app dev platforms are a mix of low-code, non-code, automation and machine learning capabilities, offer out-of-the-box DevOps capabilities, and manage dev lifecycle into efficient processes. Together, you can accelerate the development process and improve the productivity and quality of development efforts.

Today, there are many options available to support application development and integration for organizations looking for technology core competencies.

Does that mean that many organizations can develop, support and enhance applications without compromising software development processes? Can CIAs accelerate application modernization and build low-tech applications? These will be questions in the next few years, but there are many options today to support application development and integration for organizations looking for technology core competencies.

[ Want more insights on emerging technology and digital transformation? Read more from Isaac Sacolick. ]

4. Driving IT efficiency by supporting multi-cloud strategies

Many CIAs have a growing financial problem: they are adding new technologies to quickly support data systems, machine learning and cloud native applications, they can shut down older systems, data centers and business processes. Large enterprise CEOs have lived in mixed clouds for many years, and many see the multi-cloud architecture and operations as a strategy to provide greater flexibility for their businesses.

The only way for CEOs to support the financial gap or the growing technology portfolio is through efficiency in IT systems. Simply put, IT operations should support a wide variety of computing platforms without incurring high service objectives and inconsistent costs.

How can this be done?

  • Automatically set up additional IT tasks and process processes for most standard operations
  • Using AIOps tools to improve event management KPI
  • Invest in DevOps, CI / CD, IAS, Automated Testing and Left-handed security practices
  • Integrate ITSM, DevOps, SRE and efficient tools and processes to improve collaboration
  • Choosing “one-size-fits-all” devices that work on public and private clouds

Investors who invest heavily in digital transformation can create impossible technical debt.

[ Read also: 6 misconceptions about AIOps, explained. ]

5. Replace fall T PMOs with efficient value stream management

Every year, I recommend that CIAs stop overeating, reduce their number one priority, and move to a more efficient approach to the Program Management Office (PMO). It is important to prepare road maps and show business value for CIAs that have been leading digital transformation journeys for many years, but asking teams to provide top-down strategic plans and quarterly planning is the opposite of driving dynamic cultures, processes and mindsets.

CEOs cannot be divided between effective practices that focus on real change and business leaders’ strategic priorities and how they manage business maps. Driving the organization’s culture requires that CEOs work directly with their efficient teams, take further planning experience, and evaluate emerging asset management tools.

Digital transformation failures and rebounds are on the rise. IT leaders need to explore and improve the trends behind their programs in order to be successful.

[ Culture change is the hardest part of digital transformation. Get the digital transformation eBook: Teaching an elephant to dance. ]